Abstract
There is spiking interest in graph analysis, mainly sparked by social network analysis done for various purposes. With social network graphs often achieving very large size, there is a need for capable tools to perform such an analysis. In this article, we contribute to this area by presenting an original approach to calculating various graph morphisms, designed with overall performance and scalability as the primary concern. The proposed method generates a list of candidates for further analysis by first decomposing a complex network into a set of sub-graphs, transforming sub-graphs into intermediary structures, which are then used to generate grey-scaled bitmap images, and, eventually, performing image comparison using Fast Fourier Transform. The paper discusses the proof-of-concept implementation of the method and provides experimental results achieved on sub-graphs in different sizes randomly chosen from a reference dataset. Planned future developments and key considered areas of application are also described.
Highlights
Social networks play an essential role in everyone’s lives
The problem of identifying graph morphisms is usually solved by a time- and memoryexpensive algorithm [9] or various application-specific algorithms, such as Frequent Subgraph Mining (FSM) algorithms [10]
Big data graphs whose size keeps growing with every year, reducing execution time and memory consumption becomes a concern of increasing importance
Summary
Social networks play an essential role in everyone’s lives. People get involved in social networks for various reasons, such as leisure, hobby, or work. The social network data obtained from such platforms can be effectively used, e.g., to improve team structure and performance [1] or help in the information systems requirements elicitation process [2]. As social network graphs may achieve a very large size, analyzing them often becomes a highly time-consuming process. This motivates the search for new time-efficient methods for graph analysis. We were directly motivated by the need to analyze user interactions in team collaboration platforms by identifying cliques and similarities in user behaviors that may adversely impact business processes (e.g., hurt software development quality and costs), the proposed method can as well be used for any other analytical purposes. The final section of the paper summarizes the findings, and the steps to follow are given
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